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Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. IntroductionHello, fellow R enthusiasts! I’m thrilled to share some fantastic updates to the TidyDensity package. These updates bring a wealth of new features, functions, and enhancements, making distribution analysis more comprehensive and efficient. Let’s dive into the details!
New FeaturesNegative Binomial Distribution util_negative_binomial_aic()*: Calculate the Akaike Information Criterion (AIC) for the negative binomial distribution. This function aids in model selection, helping you determine the best-fitting model for your data.
Zero-Truncated Negative Binomial Distribution util_zero_truncated_negative_binomial_param_estimate(): Estimate the parameters of the zero-truncated negative binomial distribution. * util_zero_truncated_negative_binomial_aic(): Calculate the AIC for the zero-truncated negative binomial distribution. * util_zero_truncated_negative_binomial_stats_tbl()*: Create a summary table for the zero-truncated negative binomial distribution.
Zero-Truncated Poisson Distribution util_zero_truncated_poisson_param_estimate(): Estimate the parameters of the zero-truncated Poisson distribution. * util_zero_truncated_poisson_aic(): Calculate the AIC for the zero-truncated Poisson distribution. * util_zero_truncated_poisson_stats_tbl()*: Create a summary table for the zero-truncated Poisson distribution.
F Distribution util_f_param_estimate(): Estimate the parameters for the F distribution. * util_f_aic()*: Calculate the AIC for the F distribution.
Zero-Truncated Geometric Distribution util_zero_truncated_geometric_param_estimate(): Estimate the parameters of the zero-truncated geometric distribution. * util_zero_truncated_geometric_aic(): Calculate the AIC for the zero-truncated geometric distribution. * util_zero_truncated_geometric_stats_tbl()*: Create a summary table for the zero-truncated geometric distribution.
Triangular Distribution util_triangular_aic()*: Calculate the AIC for the triangular distribution.
T Distribution util_t_param_estimate(): Estimate the parameters of the T distribution. * util_t_aic()*: Calculate the AIC for the T distribution.
Pareto Type I Distribution util_pareto1_param_estimate(): Estimate the parameters of the Pareto Type I distribution. * util_pareto1_aic(): Calculate the AIC for the Pareto Type I distribution. * util_pareto1_stats_tbl()*: Create a summary table for the Pareto Type I distribution.
Paralogistic Distribution util_paralogistic_param_estimate(): Estimate the parameters of the paralogistic distribution. * util_paralogistic_aic(): Calculate the AIC for the paralogistic distribution. * util_paralogistic_stats_tbl()*: Create a summary table for the paralogistic distribution.
Inverse Weibull Distribution util_inverse_weibull_param_estimate(): Estimate the parameters of the Inverse Weibull distribution. * util_inverse_weibull_aic(): Calculate the AIC for the Inverse Weibull distribution. * util_inverse_weibull_stats_tbl()*: Create a summary table for the Inverse Weibull distribution.
Inverse Pareto Distribution util_inverse_pareto_param_estimate(): Estimate the parameters of the Inverse Pareto distribution. * util_inverse_pareto_aic(): Calculate the AIC for the Inverse Pareto distribution. * util_inverse_pareto_stats_tbl()*: Create a summary table for the Inverse Pareto distribution.
Inverse Gamma Distribution util_inverse_burr_param_estimate(): Estimate the parameters of the Inverse Gamma distribution. * util_inverse_burr_aic(): Calculate the AIC for the Inverse Gamma distribution. * util_inverse_burr_stats_tbl()*: Create a summary table for the Inverse Gamma distribution.
Generalized Pareto Distribution util_generalized_pareto_param_estimate(): Estimate the parameters of the Generalized Pareto distribution. * util_generalized_pareto_aic(): Calculate the AIC for the Generalized Pareto distribution. * util_generalized_pareto_stats_tbl()*: Create a summary table for the Generalized Pareto distribution.
Generalized Gamma Distribution util_generalized_beta_param_estimate(): Estimate the parameters of the Generalized Gamma distribution. * util_generalized_beta_aic(): Calculate the AIC for the Generalized Gamma distribution. * util_generalized_beta_stats_tbl()*: Create a summary table for the Generalized Gamma distribution.
Zero-Truncated Binomial Distribution util_zero_truncated_binomial_stats_tbl(): Create a summary table for the Zero Truncated binomial distribution. * util_zero_truncated_binomial_param_estimate(): Estimate the parameters of the Zero Truncated binomial distribution. * util_zero_truncated_binomial_aic()*: Calculate the AIC for the Zero Truncated binomial distribution.
Minor Improvements and Fixes util_negative_binomial_param_estimate(): Updated to use optim() for parameter estimation, enhancing accuracy and efficiency.
* quantile_normalize()*: Added names to columns when .return_tibble = TRUE for better readability and usability.
ConclusionThese updates significantly expand the functionality of TidyDensity, providing more tools for robust distribution analysis. Whether you’re working with standard or specialized distributions, these new functions and improvements will streamline your workflow and enhance your analytical capabilities.
I encourage you to explore these new features and see how they can benefit your projects. As always, your feedback is invaluable, so please share your thoughts and experiences with these updates. Happy coding!
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